"Smallytics™" algorithm that makes it easy to use "Causal Discovery" which requires supercomputer class processing

Discovering the structure of causal relationships from data is called "Causal Discovery," and estimating causal effects is called "Causal Inference." When the structure of the causal relationship is known, "Causal Inference" can be conducted promptly. However, in many cases, uncovering unknown relationships drives innovation. In such instances, "Causal Discovery" proves to be a valuable tool. Yet, until now, this approach has been impractical due to the substantial increase in processing time required to compute all data combinations when estimating causal structures. This process could take hours, days, or even necessitate the use of a supercomputer in extreme cases. xCausal™ addresses this challenge with its unique Smallytics™ algorithm. This algorithm assesses and prioritizes data interrelationships, considering the assumptions underlying causal relationships across various combinations of time-series data.

Smallytics™ suggests variables that are strongly related to the data you are analyzing. Users can then refine their selection by choosing the most meaningful variables (up to 10 nodes, customization available). As a result, it is possible to dramatically reduce the amount of combinatorial calculations and instantly generate causal graphs with network structures in tens of seconds or minutes. Therefore, users can iterate through trial and error with data to understand cause-and-effect relationships and formulate high-resolution hypotheses for solutions. Users can select from three types of causal discovery algorithms: Score based, constraint based, and hybrid.

A future-oriented SaaS service that makes academic know-how easy to use and has excellent functionality enhancement and scalability.

"xCausal™" is a SaaS that can be used by uploading data. xCausal™ allows users to easily perform missing data completion and create rule tables when uploading data. Smallytics™, the analysis engine, is resilient to missing data and offers user-friendly functionality, allowing users to utilize it without investing time in the data cleansing process typically required for statistical analysis and AI. Another unique feature is its intuitive and sophisticated UI design, making it user-friendly despite the complexity often associated with such interfaces. After data upload, "Causal Discovery" and "Causal Inference" can be quickly and seamlessly performed, facilitating the application of academic expertise.

Compared to PC-installed software, this forward-thinking solution offers scalability in processing, facilitates collaborative data analysis, and provides continuous updates to evolving Causal AI functions. It is also more practical than software installed on in-house cloud systems, as it does not require coding implementation and can be used without incurring engineering costs.

Customization, Accompaniment Professional Services and Partnerships.

Depending on how "Causal Discovery" and "Causal Inference" are utilized, arriving at a viable solution may be challenging. Specifically, without obtaining the data potentially causing the issue, identifying a causal relationship becomes improbable. We provide professional services including consulting, data analysis, and customization to resolve these customer issues. Our advisory committee, comprising professors and associate professors from leading universities and research institutions, conducts cutting-edge research in various data science areas, including causal inference, machine learning, statistical analysis, and LLM, continuously generating new knowledge. Apart from customizing xCausal™ functionality, the system also facilitates diverse development avenues, such as applying outputs from causal models, integrating them with predictive models, and utilizing them alongside generative AI. Moreover, we aim to enhance our contribution to resolving client issues by collaborating with partner companies possessing expertise across diverse industries and domains in the near future.